Glossary
Network term
Correlation-based network (CN)
Clique
Community
Community detecting algorithm (CDA)
Modularity score
Fast-greedy community algorithm
(FCA)
(Clauset et al. 2004).
Geodesic distance
Walktrap community algorithm (WCA)
(Pons and Latapy 2005)
Edge betweenness community
algorithm (EBCA)
(Newman and Girvan 2004)
Definition
CNs are obtained by applying (Pearson)
correlations on the data profiles from
considered components. In biological CNs,
nodes correspond to the biological
components, and edges are established based
on correlations between the corresponding data
profiles.
A clique is a maximal complete subnetwork, in
which each pair of nodes is connected by an
edge.
A community is a collection of densely or
strongly connected nodes relatively to their
relation with the rest of the network.
Communities are detected by community
detecting algorithms.
A CDA detects communities in a network
derived on principles observed in the physical,
biological, applied mathematical,
computational, and social sciences.
A modularity score quantifies the strength of
the division of a network into communities
based on the CDA applied.
The FCA applies a hierarchical bottom-up
approach, in which initially each node is
separated into a different community.
Iteratively, communities are logically merged
until an optimal modularity score is achieved.
The geodesic distance between two nodes
is the length of the shortest path (i.e. geodesic)
between them.
The WCA is based on the idea of short random
walks, where nodes are merged into
communities based on the similarity of their
corresponding random walks.
The EBCA, also known as the GirvanNewman algorithm, is based on the edgebetweenness centrality property, which is given
by the number of geodesic distances
between any two nodes that contain a given
edge. The EBCA applies a hierarchical
decomposition process, in which edges are
removed based on their betweenness score.
Communities are build on the idea that edges
connecting different communities are more
Weighted node degree (WND)
Global clustering coefficient (GCC)
Assortativity coefficient (AC)
likely to be contained as multiple shortest
paths.
The WND quantifies the weight of all edges
incident on a node. In case of a CN the weight
of an edge corresponds to the absolute
correlation coefficient.
The GCC is defined as the ratio of all closed
triplets over all triplets possible in a network. It
assesses how close the network is from being a
fully connected network.
The AC is the correlation between the node of
degree k and the average of neighbor degree
over all nodes of degree k. It tells in a concise
fashion how nodes are preferentially connected
to each other.